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MI-MCF: A Mutual Information-Based Multilabel Causal Feature Selection

IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 9864-9878页

作者： Lin Ma Liang Hu Yonghao Li Weiping Ding Wanfu Gao Department of Computer Science and Technology Jilin University Changchun Jilin China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu Sichuan China School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

Multilabel causal feature selection has attracted extensive attention in recent years. Current multilabel causal feature selection algorithms typically employ existing Markov Blanket (MB) search methods for the initial construction of the MB, followed by further optimization. These methods generally treat labels and features as equally weighted nodes during the MB construction process. However, the search for spouse sets often involves extensive conditional independence (CI) tests, which are time-consuming. Furthermore, they fail to consider the distinct contributions of labels and features to the target nodes. Information theory is often used to evaluate the contributions of nodes. Inspired by this, we carry out a theoretical investigation into the causal relationships within multilabel datasets and propose the mutual information-based multilabel causal feature selection (MI-MCF) method. First, MI-MCF employs MI and conditional MI (CMI) instead of CI test when constructing the MB of labels without incurring significant time overhead. Then, MI-MCF uses MI to compare the contributions of features and labels to the target nodes. This helps identify which nodes should be retained when recovering features hindered by strong label correlation. Finally, MI-MCF eliminates spurious nodes through a symmetry check. Experiments on real-world datasets demonstrate that MI-MCF can autonomously determine the optimal number of selected features and consistently outperform compared methods. The code is available at https://***/malinjlu/MI-MCF .

关键词： Feature extraction Correlation Learning systems Redundancy Optimization Mutual information Manifolds Electronic mail Computational complexity Accuracy

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Healthcare Cost Patterns and Prediction: Investigating Personal datasets Using data Analytics 3rd

Healthcare Cost Patterns and Prediction: Investigating Perso...

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3rd International Conference on Signal and data Processing, ICSDP 2023

作者： Islam, Md. Aminul Nag, Anindya Chandra, Pretam Mishra, Bhupesh Kumar Fahim, S. M. Firoz Ahmed Hoque, Md Mozammel School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom College of Engineering and Physical Sciences University of Birmingham Birmingham United Kingdom Computer Science & Engineering Discipline Khulna University Khulna9208 Bangladesh Department of Computer Science & Engineering Adamas University Kolkata700126 India Centre of Excellence for Data Science AI and Modelling University of Hull Hull United Kingdom Department of Computer Science & Engineering American International University Dhaka Bangladesh Gannon University Erie United States

ISBN: (纸本)9789819795772

The present study introduces a health insurance prediction system that leverages machine learning methodologies. In contemporary times, there has been a notable increase in endeavors focused on tackling this matter, since the significance of health insurance as a research topic has markedly escalated following the occurrence of the pandemic. The dataset employed in this research comprises 1338 observations, 7 columns, and corresponds to individual medical expenditures in the United States which is available at the Kaggle platform. The dataset encompasses a variety of variables utilized in the prediction of insurance prices, including age, gender, BMI, smoking status, and number of children. The researchers utilized machine learning models including neural network, XAI, auto modeling to determine the correlation between pricing and the aforementioned attributes. The training process involved partitioning the dataset into a 80–20 ratio for training and evaluation respectively. As a consequence, the system achieved an accuracy rate of 97% by Gradient Boosting but we corrected it as 92% by Gradient Boosting Regressor by encoding and hyper tuning. Also, among predictive machine learning models, Random Forest had the best accuracy i.e., of 83.44%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Health insurance

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Embedding Vendor Certifications in the Cloud computing Curriculum: Scope, Strategy and Perceptions 22

Embedding Vendor Certifications in the Cloud Computing Curri...

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27th ACM Conference on Innovation and Technology in Computer science Education, ITiCSE 2022

作者： Paterson, James H. Adams, Joshua Foster, Derek Baghban Karimi, Ouldooz Kazmi, Zain Lennon, Ruth G. Nazir, Sajid Stott, Lee White, Laurie Department of Computing Glasgow Caledonian University Glasgow United Kingdom Saint Leo University School of Computing Artificial Intelligence Robotics and Data Science Saint LeoFL United States University of Lincoln School of Computer Science Lincoln United Kingdom Simon Fraser University School of Computing Science Simon Fraser University Surrey SurreyBC Canada University of Toronto TorontoON Canada Atlantic Technological University Department of Computing Letterkenny Ireland Microsoft SeattleWA United States Google LLC SeattleWA United States

ISBN: (纸本)9781450392006

A series of previous Working Groups has met at ITiCSE conferences to explore ways of incorporating cloud computing into courses and curricula, including mapping industry job skills to knowledge areas (KAs) and KAs to student learning objectives (LOs). The importance of industry-standard learning content and certification, produced by cloud vendors and others, was apparent throughout this work. This Working Group will focus on the role of certification within cloud computing curricula, from the viewpoints of a range of stakeholders: students, graduates, institutions, vendors and other certification providers;and employers. Areas for study will include: the scope of available certifications and their mapping to our KAs and LOs;approaches to integrating certification in academic cloud curricula, and challenges involved in doing so;and perceptions of the stakeholders of the role and value of certifications in evidencing employability. The outcome of the work will include a set of recommendations for best practice. © 2022 Owner/Author.

关键词： Cloud computing

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Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation

arXiv

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arXiv 2024年

作者： Čevora, Kate Glocker, Ben Bai, Wenjia Department of Computing Imperial College London United Kingdom Department of Brain Sciences Imperial College London United Kingdom Data Science Institute Imperial College London United Kingdom

Deep learning-based medical image segmentation has seen tremendous progress over the last decade, but there is still relatively little transfer into clinical practice. One of the main barriers is the challenge of domain generalisation, which requires segmentation models to maintain high performance across a wide distribution of image data. This challenge is amplified by the many factors that contribute to the diverse appearance of medical images, such as acquisition conditions and patient characteristics. The impact of shifting patient characteristics such as age and sex on segmentation performance remains relatively under-studied, especially for abdominal organs, despite that this is crucial for ensuring the fairness of the segmentation model. We perform the first study to determine the impact of population shift with respect to age and sex on abdominal CT image segmentation, by leveraging two large public datasets, and introduce a novel metric to quantify the impact. We find that population shift is a challenge similar in magnitude to cross-dataset shift for abdominal organ segmentation, and that the effect is asymmetric and dataset-dependent. We conclude that dataset diversity in terms of known patient characteristics is not necessarily equivalent to dataset diversity in terms of image features. This implies that simple population matching to ensure good generalisation and fairness may be insufficient, and we recommend that fairness research should be directed towards better understanding and quantifying medical image dataset diversity in terms of performance-relevant characteristics such as organ morphology. © 2024, CC BY.

关键词： Image segmentation

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Efficient Edge computing: Harnessing Compact Machine Learning Models for Workload Optimization

Efficient Edge Computing: Harnessing Compact Machine Learnin...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Pour-Hosseini, Mohammad Reza Abbasi, Mahdi Javadi, Bahman Benelghali, Seifeddine Haghighi, Hassan Mohammadi-Pasand, Ehsan Moradi, Parham Department of Computer Engineering Bu-Ali Sina University Hamedan Iran LIS UMR CNRS 7020 Aix-Marseille University Marseille France Depertment of Computing Science Umeà University Umea Sweden School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia School of Engineering RMIT University Melbourne Australia

ISBN: (纸本)9798400704956

This paper proposes an innovative lightweight workload distribution model, TinyXCS, specifically designed for traditional edge devices, emphasizing efficiency and compactness. TinyXCS, an online model based on XCS learning classifier systems, demonstrates high levels of performance in reducing delays and energy consumption. Engineered to operate efficiently on conventional edge devices, The model offers a promising solution for optimizing workload distribution while considering memory constraints. Experimental results validate effectiveness of this model, representing a significant advancement in the quest for streamlined edge computing solutions. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Potholes Detection Using Yolo Algorithms and Their Integration Into Gazebo Simulation

Potholes Detection Using Yolo Algorithms and Their Integrati...

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International Conference on Cybernetics, Robotics and Control (CRC)

作者： M. Hassan Tanveer Zainab Fatima Tanazzah Rehman Moaaz Siddique Arbaz Shah Razvan Cristian Voicu M. Farrukh Shahid Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta USA Department of Software Engineering NED University of Engineering and Technology Karachi Pakistan Department of AI and Data Science FAST University of computing and Emerging Science Karachi Pakistan

ISBN: (数字)9798331517489

ISBN: (纸本)9798331517496

Road cavities, often called potholes, pose a major problem for our transportation infrastructure, causing accidents, costly repairs, and traffic jamming. This study shows using a computer vision technique called You Only Look Once (YOLO) to find potholes on roads. YOLO is proficient and efficient for realtime object detection. The researchers want to see which version of YOLO is more effective in object analysis in pictures and videos. We'll be comparing YOLOv5, YOLOv7, and YOLOv8, thereby also setting up the system in Gazebo simulated environment. By carefully analyzing the results, we aim to understand the advantages and disadvantages of each YOLO version. This will help us see the most useful ones for finding potholes when managing and maintaining roads.

关键词： YOLO Location awareness Roads Transportation Maintenance engineering Prediction algorithms Classification algorithms Object recognition Jamming Videos

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Securing Emerging IoT Environments with Super Learner Ensembles

Securing Emerging IoT Environments with Super Learner Ensemb...

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Cyber Resilience (ICCR), International Conference on

作者： Abdelraouf Ishtaiwi Ali Al Maqousi Amjad Aldweesh Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Information Security University of Petra Amman Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This paper investigates the efficacy of the Super Learner ensemble algorithm for robust anomaly detection in Internet of Things (IoT) network traffic. The recently released CIC IoT dataset 2023, which contains both normal background traffic and common cyber attack patterns, is utilized as an evaluation benchmark. The Super Learner ensemble integrates five diverse base classifiers - Support Vector Machines (SVMs), logistic regression, neural networks, random forests, and K-Nearest Neighbors (KNNs). Extensive empirical analysis is conducted by training on a large stratified sample and testing generalization performance on a held-out set. Results demonstrate that the heterogeneous Super Learner ensemble achieves 94.2% test accuracy in detecting anomalies, which significantly outperforms any individual base model by over 7 percentage points. Precision, recall, and F1 metrics are also markedly improved by the ensemble approach compared to single-learner solutions.

关键词： Measurement Training Support vector machines Telecommunication traffic Benchmark testing Classification algorithms Internet of Things

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A stable multiplicative dynamical low-rank discretization for the linear Boltzmann-BGK equation

arXiv

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arXiv 2024年

作者： Baumann, Lena Einkemmer, Lukas Klingenberg, Christian Kusch, Jonas University of Wuerzburg Department of Mathematics Wuerzburg Germany University of Innsbruck Numerical Analysis and Scientific Computing Innsbruck Austria Norwegian University of Life Sciences Department of Data Science Ås Norway

The numerical method of dynamical low-rank approximation (DLRA) has recently been applied to various kinetic equations showing a significant reduction of the computational effort. In this paper, we apply this concept to the linear Boltzmann-Bhatnagar-Gross-Krook (Boltzmann-BGK) equation which due its high dimensionality is challenging to solve. Inspired by the special structure of the non-linear Boltzmann-BGK problem, we consider a multiplicative splitting of the distribution function. We propose a rank-adaptive DLRA scheme making use of the basis update & Galerkin integrator and combine it with an additional basis augmentation to ensure numerical stability, for which an analytical proof is given and a classical hyperbolic Courant–Friedrichs–Lewy (CFL) condition is derived. This allows for a further acceleration of computational times and a better understanding of the underlying problem in finding a suitable discretization of the system. Numerical results of a series of different test examples confirm the accuracy and efficiency of the proposed method compared to the numerical solution of the full *** Codes 35Q20, 65L04, 65M12, 65M22 © 2024, CC BY-NC-ND.

关键词： Boltzmann equation

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Cloud computing in Smart Cities: Privacy, Ethical and Social Issues 1

Cloud Computing in Smart Cities: Privacy, Ethical and Social...

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1st International Conference on Computer science and Emerging Technologies, CSET 2023

作者： Alkhazali, Abdel Rahman Mohammad Khasawneh, Ahmad M. Alzoubi, Sharaf Magableh, Murad Mohamed, Rajina R. Pandey, Bishwajeet Irbid National University Faculty of Business and Financial Science MIS Bi and Data Analysis Department Irbid Jordan Department of Information Technology Jordan College of Computing Dan Informatics Universiti Tenaga Nasional Malaysia Eurasian National University Astana It University Astana Kazakhstan Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

The rapid development of cloud computing technologies has revolutionized various sectors, and smart cities are no exception. Smart cities leverage cloud computing to optimize urban services, enhance resource management, and improve the overall quality of life for their inhabitants. However, as these technological advancements proliferate, concerns about privacy, ethical considerations, and social implications have emerged. This research paper critically examines the multifaceted challenges associated with the integration of cloud computing in smart cities, shedding light on the potential risks and highlighting the need for comprehensive solutions. The research employs a mixed-methods approach, combining quantitative data analysis and qualitative case studies to offer a comprehensive perspective on the identified issues. The primary focus lies in identifying privacy risks, ethical dilemmas, and social disparities that arise due to the extensive use of cloud-based systems and data in smart cities. Furthermore, the study investigates the role of key stakeholders, including governments, technology providers, and citizens, in mitigating or exacerbating these challenges. Key findings reveal that while cloud computing empowers smart cities with unparalleled capabilities, it also exposes residents' personal information to potential breaches and misuse. Ethical concerns arise from the handling of sensitive data, data ownership, and algorithmic biases that could perpetuate discrimination. Moreover, social issues like the digital divide and access disparities may further exacerbate existing inequalities in smart city implementation. This research paper concludes by proposing a comprehensive framework of guidelines and best practices to address the identified issues effectively. These recommendations encompass enhanced data privacy measures, transparent and accountable data governance, the promotion of ethical data usage, and inclusive strategies to bridge social disparities. B

关键词： Internet of things

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Detection of construction worker safety protection equipment based on fast YOLO

Detection of construction worker safety protection equipment...

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2024 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Li, Xin Hussin, Norriza Ismail, Nor Alina Faculty of Engineering Built Environment & Information Technology SEGi University Selangor Darul Ehsan 547810 Malaysia Faculty of Data Science and Computing University Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Research and Development Hebei Voin Environmental Protection Technology Co. Ltd. Hebei054000 China

ISBN: (纸本)9781510682597

Enterprises are often concerned about disasters, particularly accidents involving personnel. Therefore, there is ongoing research dedicated to safeguarding the safety of employees within enterprises. In recent years, with the continuous development of machine vision technology, industry and academia have been working to adopt machine vision methods to address safety hazards in workers' production. Machine vision applications are still being researched for specific sectors and lack generalization. Algorithms commonly face issues of high computational complexity and demanding hardware requirements. This paper adopts the lightweight YOLOV5 as the baseline algorithm and enhances its accuracy using a receptive field attention mechanism. SeNet is introduced to improve the generalization of object detection, and IDetect Head is employed to increase the efficiency of the detection head. Ultimately, the algorithm's accuracy is enhanced by 3.7%, and mAP50 is increased by 3.0%. This algorithm can be deployed to Internet of Things (IoT) machine vision terminals, reducing deployment costs and improving monitoring efficiency. © 2024 SPIE.

关键词： Construction equipment

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